Tests that evaluating an expression with an exception in the console provide correct exception information.


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Arsenal, in full Arsenal Football Club, byname the Gunners, English professional football (soccer) team based in London. Arsenal is one of the most successful squads in English football history, having played in the country’s top division (Football League First Division to 1992, Premier League thereafter) each season since 1919. In the process it has captured 13 league titles. The club was founded in 1886 and took the name Royal Arsenal after its first game, combining the moniker of the Royal Oak pub, where the team members met, with that of their workplace, the Arsenal munitions factory in Woolwich. The name was changed to Woolwich Arsenal in 1891, and Woolwich was dropped from the name after the 1912–13 season, when the team moved its home stadium to the Highbury section of the London borough of Islington. The club played at Arsenal Stadium (commonly referred to as “Highbury”) until 2006, when it relocated to a new, 60,000-seat stadium in Islington’s Holloway district. Arsenal has a long-standing rivalry with another North London club, Tottenham Hotspur, against whom it plays the “North London derby” match nearly every year. When the Football League resumed play in 1919 after World War I, Arsenal—which had finished fifth in the Second Division before the war—was controversially promoted to the First Division over higher-placing Tottenham after Arsenal’s chairman argued that his club deserved promotion because of its longer history, further spurring the rivalry between the two teams. While Arsenal has remained in the top division ever since its contentious promotion, its periods of great achievements have been widely dispersed. The club won five league championships in the 1930s but only three total in the 50 seasons from 1938–39 to 1987–88. Arsène Wenger became the team’s manager in 1996 and has served longer in that role than anyone else in club history. Arsenal went undefeated in the 38 matches of the 2003–04 season, becoming just the second top-division English club to do so, and it set a national record by extending its unbeaten streak into the next season to 49 consecutive league contests in total. In addition to its league championships, Arsenal has won the Football Association (FA) Cup 13 times and the League Cup twice, as well as the European Cup Winners’ Cup (1994). Among the standout footballers who have played for Arsenal are forwards Cliff Bastin and Dennis Bergkamp, goalkeeper Pat Jennings, midfielder Liam Brady, defender Tony Adams, and, arguably the team’s greatest player, striker Thierry Henry, who scored a club-record 226 goals between 1999 and 2007.


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I am a Python Dev and am on my way to complete The 100 Days Code Challange Of Python and become a expert Pythonista
Intrested in Backend Developing. Making my way through the difficult bugs and errors!! But it's Fun!!
I have many hobbies and one of them are programming. I love programming. I want to make my nation the most developed Country in the world!!!
I also love studying and i love my family and the people who help and support me. Thank you!!!


the Gene Ontology
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The human brain is hard-wired to misevaluate others. Unconscious biases - snap judgments of our subconscious - systematically distort evaluations of people's skills, achievements, performances, and cultural fit. Standard recruiting processes contain no filter for biases. This deprives companies of some of the best candidates and prevents them from achieving their diversity goals.
>> We design recruiting processes that keep biases in check. <<
Based on the latest research in psychology, the cognitive sciences, and behavioral economics, we work with the following modules for unbiased recruiting.
/// Anonymization ///
diversity markers are hidden during the evaluation process/// Work samples ///
candidates provide work samples that closely match the job profile/// Cross evaluation ///
recruiters evaluate selected sets of information across all candidates/// Team evaluation ///
evaluation scores of multiple recruiters are aggregated/// Structured interviews ///
candidates answer a fixed list of carefully designed interview questionsBy supporting the implementation of unbiased recruiting processes we help you hire the best candidates and achieve your diversity goals along the way.


You can see the Video.js Info Overlay plugin in action below. Look at the source of this page to see how to use it with your videos.
Your browser doesn't support video. Please upgrade your browser to see the example.


Great Nigerians! if eventually we stand up for what we really believe in we will be unstoppable.
Say 'NO' to electoral crime
Together we can and we will.
Great Nigerians! if eventually we stand up for what we really believe in we will be unstoppable.
Together we can and we will.


TABLE OF CONTENT
My current project has reached the point where we have to manage our infrastructure in a more organized way rather than ad-hoc manual configurations.
Current landscape #
We use a pretty standard setup for a .NET web application.
We keep our code in one monorepo hosted in internal GitLab. We use TeamCity to build our code and we use Octopus Deploy to run our deployments to Azure.
We have several environments (let’s call them feature
, test
, and prod
) and they all are different. Each one has a different configuration, different accounts, different topology, different numbers of replicas and shards, you name it.
Things like connection strings, account and passwords are managed by Octopus.
At this point, we would like to utilize the configuration management feature provider by Octopus, and that’s why we would have to jump through several hoops to marry terraform configs and Octopus variable substitution mechanism.
Scope of the problem #
We’d like to move to an Infrastructure-as-a-Code approach and provisioning our system automatically as a part of our CI/CD pipeline.
To do that we’re going to add Terraform to the game.
At the time I’m writing this post the versions for the software are the following:
- TeamCity 2019.2.2
- Octopus Deploy v2019.12.0 LTS
- Terraform v0.12.24
azurerm
provider 2.2.0
Repository Setup #
This part is not very important, but to provide you with the full picture I’ll put it here.
We keep our Terraform files in the same repository alongside the application code and database schemas. Basically, it’s just an \infrastructure
directory in the root of the repo.
Terraform Setup #
We run our application in Azure, hence the choice of Terraform backend and a provider: we keep Terraform state in Azure.
Please refer to the official documentation for a detailed step by step guide on setting up storage for Terraform State.
In short, you’d need to run the following:
#!/bin/bash
RESOURCE_GROUP_NAME=terraformstate-rg
STORAGE_ACCOUNT_NAME=terraformstateci
CONTAINER_NAME=terraformstate
# Create resource group
az group create --name $RESOURCE_GROUP_NAME --location eastus
# Create storage account
az storage account create --resource-group $RESOURCE_GROUP_NAME --name $STORAGE_ACCOUNT_NAME --sku Standard_LRS --encryption-services blob
# Get storage account key
ACCOUNT_KEY=$(az storage account keys list --resource-group $RESOURCE_GROUP_NAME --account-name $STORAGE_ACCOUNT_NAME --query [0].value -o tsv)
# Create blob container
az storage container create --name $CONTAINER_NAME --account-name $STORAGE_ACCOUNT_NAME --account-key $ACCOUNT_KEY
echo "storage_account_name: $STORAGE_ACCOUNT_NAME"
echo "container_name: $CONTAINER_NAME"
echo "access_key: $ACCOUNT_KEY"
Once you’ve successfully created a storage for your state you can update your main.tf
file with the backend configuration:
For the purpose of this blog post I’ll be creating just a new resource group with one SQL server.
Let’s declare our setup in a new sql.tf
file:
note that I’m using variables in a gist above.
Variables are defined in a variables.tf
file:
variable "sql_server_admin_pwd" {
type = string
}
This works fine, but for the local testing I keep the terraform.tfvars
files with the values:
resource_group_name = "test_rg"
sql_server_admin_name = "admin_name"
sql_server_admin_pwd = "P@$$w0rd1!" # dummy pwd used for local tests
values are not encrypted, but they are just for the local tests and experiments. They will be replaced with the environment-specific vars by Octopus later.
TeamCity Setup #
Octopus has a specific syntax for variable substitution. Usually, it looks for #{Variable}
markers in your config files. As you can imagine they are not always valid as resource names.
As I mentioned before our terraform.tfvars
has realistic values for local tests. It helps our infrastructure engineers verifying their work by running terraform plan
commands.
However, when we build our packages we want to convert tfvars
file to replace variable’s values with the syntax suitable for Octopus.
I had to invent a convention here.
Every variable declaration var_name = "abc123"
will be converted to a var_name = "#{var_name}"
line.
To do that we’ve set up a build step with the following PowerShell script:
After that we can build our code and then create a release package with our Terraform config.
A TeamCity build configuration has a custom %octopus.package.suffix%
variable declared. This is a suffix that is composed of the branch name. It’s empty for the master branch build and has something like feature-AB-123
for a feature branch with an AB-123
name. Yes, this is a JIRA ticket name :)
With this variable we can prepare a zipped artifacts file:
+:infrastructure/** => Terraform.%build.number%%octopus.package.suffix%.zip
that would produce a file Terraform.1.123.9-feature-AB-123
which will later be uploaded to Octopus.
Octopus Setup #
Let’s create a new Step in Octopus project:
We currently use an official Apply a Terraform template.
However, there are few things which we had to configure in order to make it work for us.
First of all, Octopus cannot keep up with Terraform development, so you may need to update a terraform.exe
version yourself. Just grab the latest version from Terraform Downloads page and put it in a folder on your Octopus server. Something like C:\Program Files\Terraform
would do.
Then you need to provide a path to your custom terraform.exe
executable. To do that you need to set up a variable: Octopus.Action.Terraform.CustomTerraformExecutable
. Give it a C:\Program Files\Terraform\terraform.exe
value.
As you remember the release package contains a terraform.tfvars
file with templates in it. So don’t forget to tick this box:
And of course, you will have to define your variables for each environment:
note the variable name format. It should be the same as the variable names in your terraform.tfvars
file. Here we can have different values for each environment. Also, we store passwords encrypted.
And now we have a new player in our CI/CD pipeline:


... hated slideshow presentations for the fact, ...
... that the sentence you were just reading ...
... is on a slide that just vanished?
While on the good old blackboard,
everything stays for quite some time
just because the lecturer is too lazy
to wipe it out.
Well, then here is the solution.
- This is a theme for bespoke.js
- It works with any existing bespoke.js presentation
How It Works
- Slides fill half the screen or more if they contain much content
- The remaining space is used to display preceding slides in compacted form
- The theme clearly higlights the current slide
- But gives you the freedom to look back
- The slides are always in 4:3 format ... to ensure your formatting works on any screen
- But additional space is still used for the previous slides


[gecode-users] saving the way to one of the solution in a file
Mikael De Bie
mikael.debie at gmail.com
Sun Dec 3 19:25:18 CET 2006
Hi,
I'm using gecode/J 1.0.1-1 and wonder what's the best way to save the
position in the search tree in a file in order to be able to compute the
next solution later. Here's some details about what I want to do:
* launch my program the first time :
- find the first solution of a CSP
- save the way to this solution in the search tree in a file
- quit
* relaunch my program with arguments to indicate I want the next
solution :
- load the state (go to the node)
- find the next solution
- save the state (way to this solution)
- quit
I haven't found how to do that in the documentation...
Thanks a lot,
Mikaël De Bie
More information about the gecode-users
mailing list


0.6.26 (2014-06-08)
- Fixed easing functions not working when animating attributes (#533)
0.6.25 (2014-05-22)
- CommonJS (#519).
0.6.24 (2014-04-25)
- Fixed some issue with forceHeight (#347).
- Fixed a regression caused by #486 breaking IE 8 (#494).
- Added support for animating attributes (#204).
0.6.23 (2014-04-18)
note: This particular version is broken in IE!
- Experimental support for emitting events when the scrolling passes a keyframe (check out the docs for
keyframe
option/event). - When using
refresh
, make sure elements which do not longer have keyframes get properly cleaned up (#486). - Fixed
refresh
not acceptingNodeList
s (#435). - Expose the status of mobile mode as
isMobile()
function (#488).
0.6.22 (2014-02-21)
- Experimental AMD support (#409). Please read the documentation about it.
0.6.21 (2014-01-06)
- Disabled mobile mode on Windows Phone, since it's not needed there (#408).
0.6.20 (2014-01-03)
- Fixed broken percentage constants.
0.6.19 (2014-01-02)
- Constants can now be defined as functions or percentage offsets (#148, #404).
breaking: When using a constant of value 100
together with percentage offsets like data-_foo-75p
,
the value was implicitly handled as percentage value 100p
. Starting with this version you need to explcitly use 100p
if you want percentage constants.
On the plus side, you can now mix an absolute constant with a percentage keyframe or a percentage constant with an absolute keyframe.
0.6.18 (2013-12-18)
- Fixed scrolling on input elements (#397).
0.6.17 (2013-10-19)
- Fixed keyboard not appearing on some mobile browsers when an input was focused (#349).
0.6.16 (2013-10-18)
- Fixed
z-index:auto;
not working because it was always coerced to an integer (#351).
0.6.15 (2013-10-03)
- Fixed clicking on links (and other elements) on mobile (#263, #303, #338).
- Added
getMaxScrollTop
method (#238).
0.6.14 (2013-10-03)
- Fixed the
direction
parameter that's passed to the render events (#339).
0.6.13 (2013-09-29)
- Added support for percentage offsets (#185).
0.6.12 (2013-09-17)
- Added
destroy
method (#109).
0.6.11 (2013-08-13)
- Made the mobile deceleration configurable and lowered the default (#222, #229).
0.6.10 (2013-07-30)
- Fixed bug which caused IE to perform an endless loop (#271).
0.6.9 (2013-07-01)
- Improved overall performance for mobile (#249).
0.6.8 (2013-06-17)
- Added a new option
smoothScrollingDuration
.
0.6.7 (2013-06-17)
- Changed the default value of
edgeStrategy
fromease
toset
. There are too many cases whereease
was not wanted and unexpected.
0.6.6 (2013-06-05)
- Fixed IE plugin not working. This was caused by assigning
skrollr.setStyle
to a local variable inside the skrollr core. Since the IE plugin monkey-patches the skrollr.setStyle function, the core didn't notice the change (#199 comment 18986949).
0.6.5 (2013-05-22)
- Fixed crash in IE < 9 because the detected prefix was
null
(#220).
0.6.4 (2013-05-21)
- Fixed that some elements got the
skrollable-before
andskrollable-after
class at the same time.
0.6.3 (2013-05-19)
- When resizing the browser, the scroll position was reset to 0 (#217)
0.6.2 (2013-05-18)
- When resizing the browser,
forceHeight
was colliding with the already forced height (#216).
0.6.1 (2013-05-18)
- Allow numbers inside of easing function names (#152).
0.6.0 (2013-05-18)
Expect things to break when coming from 0.5! Read through the changelog. Migration is not hard.
- [breaking] There's no more
skrollr.mobile.js
file. You only needskrollr.js
. You no longer need to conditionally includeskrollr.mobile.js
. - You can configure how skrollr detects mobile browsers using the
mobileCheck
option (check out the documentation). - [possibly breaking] The meaning of the
#skrollr-body
element changed. Put all static elements inside of it and all absolute/fixed elements outside. It doesn't need to be the first child of the body anymore. - [breaking] The
rendered
andunrendered
classes where renamed because they were confusing and wrong. They're now calledskrollable-before
andskrollable-after
, because that's their meaning (the element with these classes is before/after the first/last keyframe).- Added a new class
skrollable-between
, because symmetry. That's why.
- Added a new class
- Easing functions are now applied when exactly at a keyframe (#132).
- [possibly breaking] The behavior changed for the case when the scroll position is before/after the first/last keyframe (I'm just gonna use "before first" from now on, because "after last" is analog). In 0.5 the behavior was not exactly specified and buggy (see item above regarding #132). Skrollr was applying the styles of the first keyframe to the element for all scroll position that were before the first keyframe. E.g. when
data-100="top:200px;"
was the first keyframe, the element hadtop:200px;
at all scroll positions before (all from0
to99
). From now on you can specify the behavior you want (seeedgeStrategy
option for details, set it toset
for old behavior). Note: 0.6.7 and up useset
as default.
0.5.14
- Add a
skrollr-mobile
class to the html element when the mobile script is included.
0.5.13 (2013-02-08)
-
131: Use a cross browser approach for getting the body scroll-height.
-
133: Use the maximum of document height or the max keyframe for edge cases where absolute keyframes are used in a relative-mode-like document and
data-end
was calculated wrong.
0.5.12 (2013-02-08)
-
121: Fixed prefix detection in Safari.
0.5.11 (2013-01-18)
-
126: When calling refresh(), force rerendering, even if the scrollbar didn't move.
0.5.10
-
104: Fixed the most annoying bug on mobile. There was a large blank space at the bottom of the page.
0.5.9
-
118: Fixed broken prefix detection. Was broken all the time, but worked before Firefox 18.
0.5.8 (2013-01-12)
-
116 + #117: SVG support was broken for relative mode.
0.5.7
-
103: skrollr no longer depends on being added to the bottom of the document.
0.5.6
-
105: Fixed inconsistent behaviour for adding
rendered
andunrendered
class on page load
0.5.5
-
100: Fixed relative-mode not working correctly in IE < 9 due to crippled getBoundingClientRect
0.5.4 (2012-11-18)
-
80: When resizing the browser window the calculation of relative mode was wrong when the element's vertical position was changed before.
0.5.3
-
66: Make IE 7 support a light version of
data-anchor-target
by mappingquerySelector
togetElementById
.
0.5.2
-
78: Fixed that new parser didn't allowed omitting the last semicolon in a keyframe property list.
0.5.1 (2012-10-29)
- Fixed
setScrollTop
andanimateTo
not working because iScroll uses negative offset.
0.5.0 (2012-10-09)
- breaking the
plugin
api has been removed (the IE plugin has been updated to a new, hidden api). - Full mobile support using iscroll.
-
73: Fixed parser to not mess up colons inside URLs
-
74: Fixed parser to not treat single periods as decimal numbers
-
76: Fixed dummy element overlaping the content, even though it should be unobtrusive
0.4.13
-
58:
forceHeight
now handles relative mode like a boss. -
59: Make
scale
option only affect absolute mode.
0.4.12
-
64: Setting
float
property using JavaScript didn't work across browser. Now usingstyleFloat
andcssFloat
properties.
0.4.11 (2012-09-17)
- The
scale
option does not affectconstants
.
0.4.10
- Allow smooth scrolling on element level using
data-smooth-scrolling
0.4.9
- Added experimental smooth scrolling (no more CSS transitions. WORKS IN IE.).
0.4.8
- Added
stopAnimateTo
method.
0.4.7
- Updated the requestAnimationFrame polyfill for smoother animations
- Updated the way requestAnimationFrame is used for even smoother animations
0.4.6
- New method
relativeToAbsolute
which was formerly private - New method
isAnimatingTo
to check if an animation caused byanimateTo
is running - Added
sqrt
easing function
0.4.5
- Experimental mobile support using https://github.com/zynga/scroller
0.4.4
- A
skrollr
class is added to the HTML element and ano-skrollr
class is removed wheninit
is called. Useful for fallback styling.
0.4.3 (2012-08-02)
- Added new feature "constants".
0.4.2 (2012-07-26)
- Added new feature "anchor-target" which allows elements to react to other elements leaving/entering the viewport.
0.4.1 (2012-07-25)
- Fixed a bug which broke skrollr in IE caused by wrong regular expression behavior
0.4.0 (2012-07-22)
- breaking the
data-end-[offset]
syntax changed. It's nowdata-[offset]-end
. - Fixed a bug where white spaces between style declarations were not ignored.
- Added support for anchors. Animations can now be specified relative to the elements position within the viewport.
- Added support for SVG elements.
- Added new method
refresh()
.


Contribute¶
redux-form-auto needs your help. Skins must be written!
Where to begin¶
Make a fork and do your magic followed by a pull request.
Some code pointers¶
Everything is work in progress until there’s a bunch of skins or we are in complete control of the world, whichever comes first.
The most important files:
schema.js
where the class is definedui/components/componentRender.jsx
processes the schema to build the inputs and allows skin overrides
Style¶
I just need you to follow the style of the file you are changing. If you create a file from scratch you can style it however you want provided it’s not an obfuscated code contestant.


Get a free vacation at a place where no one finds you
What you will get:
- A return flight ticket to a secret place
- A comfortable place to sleep in 4 nights (hotel, hostel, forest, etc)
- Breakfasts, lunchs, snacks, dinners ready
- €1000 as a gift
Sign up now to join the competition:


--- title: Catching up with dplyr 1.0 with fivethirtyeight data author: Corey Pembleton date: '2020-07-26' slug: catching-up-with-dplyr-1-0 categories: ["R"] tags: ["dplyr", "data cleaning"] output: blogdown::html_page: highlight: tango ---
Using the latest (July 27th 2020) COVID-19 polling data made available by fivethirtyeight, I’d like to explore some of the updates to dplyr to refresh myself on some of the changes made as I’ve been away from programming for…at least 6 months.
They have several .csv files, I’ll just pull one of them out:| name | description |
|---|---|
| covid_approval_polls.csv | contains polls that ask Americans whether or not they approve of the way Trump is handling covid-19. |
| covid_concern_polls.csv | contain the polls ask Ameicans how concerned they feel about aspects of the outbreak such as infection and economic impact. |
| covid_approval_polls_adjusted.csv and covid_concern_polls_adjusted.csv | contain the polls after adjustments are applied by our poll-averaging algorithm. |
| covid_approval_toplines.csv and covid_concern_toplines.csv | contain the calculated daily averages for the approval and concern polls respectively. |
| Name | econ |
| Number of rows | 128 |
| Number of columns | 15 |
| _______________________ | |
| Column type frequency: | |
| character | 7 |
| Date | 2 |
| logical | 1 |
| numeric | 5 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| pollster | 0 | 1.00 | 3 | 45 | 0 | 14 | 0 |
| sponsor | 100 | 0.22 | 4 | 38 | 0 | 11 | 0 |
| population | 0 | 1.00 | 1 | 2 | 0 | 3 | 0 |
| party | 0 | 1.00 | 3 | 3 | 0 | 1 | 0 |
| subject | 0 | 1.00 | 15 | 15 | 0 | 1 | 0 |
| text | 0 | 1.00 | 80 | 183 | 0 | 19 | 0 |
| url | 0 | 1.00 | 40 | 130 | 0 | 126 | 0 |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| start_date | 0 | 1 | 2020-01-27 | 2020-07-17 | 2020-04-24 | 85 |
| end_date | 0 | 1 | 2020-01-29 | 2020-07-20 | 2020-04-27 | 87 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| tracking | 0 | 1 | 0.27 | FAL: 94, TRU: 34 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| sample_size | 0 | 1 | 2674.95 | 7304.86 | 609 | 1012 | 1368.5 | 2065.25 | 53106 | ▇▁▁▁▁ |
| very | 0 | 1 | 50.59 | 11.08 | 19 | 44 | 49.0 | 59.25 | 73 | ▁▂▇▅▃ |
| somewhat | 0 | 1 | 33.46 | 6.71 | 19 | 28 | 34.0 | 39.00 | 45 | ▃▅▇▇▇ |
| not_very | 0 | 1 | 9.92 | 4.97 | 2 | 7 | 10.0 | 12.00 | 28 | ▅▇▂▁▁ |
| not_at_all | 0 | 1 | 3.83 | 2.09 | 1 | 3 | 3.0 | 5.00 | 15 | ▇▆▁▁▁ |
Checking the data with the new summarise()
functionality which allows for multiple value outputs:
econ %>%
group_by(sponsor) %>%
summarise(sd = sd(sample_size), mean = mean(sample_size)) %>%
head(10) %>%
kableExtra::kable() %>%
kable_styling()
| sponsor | sd | mean |
|---|---|---|
| Axios | 24.67903 | 1024.636 |
| Business Insider | NA | 1051.000 |
| CNBC | 173.55403 | 1289.000 |
| Fortune | 654.78572 | 2247.667 |
| Gradient Metrics | NA | 609.000 |
| NBC News | 4369.85152 | 49355.667 |
| New York Times | NA | 2593.000 |
| Protect Our Care | 105.35891 | 940.500 |
| State Science and Technology Institute | NA | 800.000 |
| Tally | NA | 2001.000 |
Another new feature is the rowwise()
function, which I believe will come very handy in the future cleaning survey data for export to excel where colleagues enjoy having row names.
Here I can create a grouped row-wise set of columns which can be useful for a dataset like this where I need to perform operations across columns:
econ %>%
filter(start_date >= "2020-06-01") %>%
rowwise(pollster) %>%
summarise(sum = sum(c_across(very:not_at_all))) %>%
head(10) %>%
kableExtra::kable() %>%
kable_styling()
| pollster | sum |
|---|---|
| Ipsos (online) | 98 |
| Global Strategy Group/GBAO/Navigator Research | 100 |
| Harris Poll | 99 |
| Morning Consult | 93 |
| SurveyMonkey | 99 |
| Ipsos (online) | 97 |
| Global Strategy Group/GBAO/Navigator Research | 100 |
| Harris Poll | 100 |
| Morning Consult | 95 |
| SurveyMonkey | 99 |
Also interesting is the introduction of c_across()
for easier grabbing columns, and across()
which applies a function or set of functions to a set of columns, like mutate_if but more intuitive, and nice because you can use other tidyverse
select verbs with it.
econ %>%
filter(start_date >= "2020-06-01") %>%
rowwise(pollster) %>%
mutate(sum = sum(c_across(very:not_at_all))) %>%
ungroup() %>%
mutate(across(very:not_at_all, ~. / sum )) %>%
head(10) %>%
select(pollster, very:not_at_all, sum) %>%
kableExtra::kable() %>%
kable_styling()
| pollster | very | somewhat | not_very | not_at_all | sum |
|---|---|---|---|---|---|
| Ipsos (online) | 0.5306122 | 0.3367347 | 0.1020408 | 0.0306122 | 98 |
| Global Strategy Group/GBAO/Navigator Research | 0.4800000 | 0.3700000 | 0.1300000 | 0.0200000 | 100 |
| Harris Poll | 0.5757576 | 0.3232323 | 0.0606061 | 0.0404040 | 99 |
| Morning Consult | 0.6559140 | 0.2795699 | 0.0430108 | 0.0215054 | 93 |
| SurveyMonkey | 0.6666667 | 0.2525253 | 0.0606061 | 0.0202020 | 99 |
| Ipsos (online) | 0.5360825 | 0.3298969 | 0.1030928 | 0.0309278 | 97 |
| Global Strategy Group/GBAO/Navigator Research | 0.4700000 | 0.3900000 | 0.1000000 | 0.0400000 | 100 |
| Harris Poll | 0.5000000 | 0.3600000 | 0.0900000 | 0.0500000 | 100 |
| Morning Consult | 0.6315789 | 0.2947368 | 0.0421053 | 0.0315789 | 95 |
| SurveyMonkey | 0.6464646 | 0.2727273 | 0.0606061 | 0.0202020 | 99 |
This is a much neater syntax then before, very nice 👍. There’s much more to learn on it, and as I dive back into R will be noting here how I use the new dplyr verbs and nouns.


Class Router
MVC Router
- Simpletools\Mvc\Common
- Simpletools\Mvc\Router
public
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protected
array
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public
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public static
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public static
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public
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public
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public
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protected
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public
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public
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public
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public
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public
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public
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public
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public
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public
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integer |
NOSLASH_NOINDEX
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#
1
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integer |
SLASH_NOINDEX
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#
2
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integer |
NOSLASH_INDEX
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#
3
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integer |
SLASH_INDEX
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#
4
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protected
string
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$_appDir
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#
''
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protected
boolean
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$_forwarded
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#
false
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protected
string
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$_params
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#
''
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protected
boolean
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$_autoRender
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true
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protected
boolean
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$_errorCode
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false
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protected
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$_error
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#
null
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array
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$_classes
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#
array()
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boolean
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$_404_error_header
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true
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boolean
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$_enableRouterHooks
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false
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boolean
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$_shifts_params
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false
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protected
boolean
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$_objects
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false
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array
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$_routingNamespaces
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#
array()
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protected
string
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$_activeRoutingNamespace
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#
''
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protected
string
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$_activeRoutingNamespaceUrlPath
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''
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protected
string
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$_view
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#
''
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protected
boolean
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$_view_enabled
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#
true
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protected
boolean
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$_customRoutes
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#
false
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protected
boolean
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$_activeCustomRouteArgs
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#
false
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protected
array
|
$_httpMethods
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#
array(
"OPTIONS" => "OPTIONS",
"GET" => "GET",
"HEAD" => "HEAD",
"POST" => "POST",
"PUT" => "PUT",
"DELETE" => "DELETE",
"TRACE" => "TRACE",
"CONNECT" => "CONNECT",
)
|


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Incorporation


Before Change
// Set up initial state, we need to add computed num qubits to params
initial_state_params = params.get(QuantumAlgorithm.SECTION_KEY_INITIAL_STATE)
initial_state_params["num_qubits"] = operator.num_qubits
initial_state = get_pluggable_class(PluggableType.INITIAL_STATE,initial_state_params["name"])
initial_state = initial_state()
initial_state.init_params(initial_state_params)
self.init_args(
operator, operator_mode, initial_state, evo_operator, evo_time, num_time_slices,
paulis_grouping=paulis_grouping, expansion_mode=expansion_mode, expansion_order=expansion_order
After Change
expansion_order = dynamics_params.get(EOH.PROP_EXPANSION_ORDER)
// Set up initial state, we need to add computed num qubits to params
initial_state_params = params.get(QuantumAlgorithm.SECTION_KEY_INITIAL_STATE)
initial_state_params["num_qubits"] = operator.num_qubits
initial_state = get_pluggable_class(PluggableType.INITIAL_STATE,initial_state_params["name"])(initial_state_params)
self.init_args(
operator, operator_mode, initial_state, evo_operator, evo_time, num_time_slices,
paulis_grouping=paulis_grouping, expansion_mode=expansion_mode, expansion_order=expansion_order


FROM: MR: HENRI MOYO<EMAIL_ADDRESS>Good day! My name is MR.HENRI MOYO, eldest son of MR.
CHARLES MOYO of Zimbabwe. This might be a surprise to
you where I got your contact address. I got your
address from South African Information Exchange in
Johannesburg.
During the current war against the farmers in Zimbabwe
from the supporters of our President Robert Mugabe to
claim all the white-owned farms to his party members
and his followers, he ordered all the white-owned
farmers to surrender all their farms to his party
members and his followers. My father was one of the
best farmers in our country and because he did not
support his idea, supporters invaded my father’s farm
and burnt everything in the farm, killing my father
and made away with a lot of items in my father’s farm.
After the death of my father, my mother and I and my
younger sister decided to move out from Zimbabwe
because our lives were in danger with the money my
father kept in his hidden safe in my mother’s house.
The amount contained in the safe is US$20m (Twenty
million, US dollars) and we decided to move to the
Republic of South Africa where we deposited the money
in a Security Company as valuables. So, I decided to
contact overseas Firm that will assist me to move this
money out of South Africa, because we are asylum
seekers here in South Africa which disqualifies us
from operating an account or getting involved in any
Financial transaction here in South Africa. We have
agreed to give you from the total sum 20% for helping
us to move this money to your country while 80% will
be for me and my family to invest in your country.
If you are interested please kindly furnish me with
Your personal phone and fax numbers for easy
Communication. You can contact me on the above
Email address, Note that this transaction is 100%
Risk-free and it requires mutual trust and absolute
Confidentiality and I have taken care of all
Formalities regarding it. Your urgent response will be
Highly appreciated in this regard.
Thanks and God bless.
Best regards,
Henri


|
xpcc
|
The View class is the base class for all screens handled by the ViewStack class. More...
#include <xpcc/ui/gui/view.hpp>
Public Member Functions | |
| View (xpcc::gui::GuiViewStack *stack, uint8_t identifier, xpcc::gui::Dimension dimension) | |
| virtual void | update () |
| May be called as often as possible. More... | |
| virtual void | preUpdate () |
| virtual void | postUpdate () |
| virtual void | draw () |
| Draw determine the output on the Graphic Display. | |
| bool | pack (Widget *w, const xpcc::glcd::Point &coord) |
| Add widget to view. | |
| void | remove () |
| Remove the view from the screen. The viewStack handles the deletion. | |
| void | setColorPalette (ColorPalette &cp) |
| Set color palette for every contained widget. | |
| ColorPalette & | getColorPalette () |
| void | markDirty () |
| void | markDrawn () |
| xpcc::gui::GuiViewStack * | getViewStack () |
| Public Member Functions inherited from xpcc::AbstractView | |
| AbstractView (xpcc::ViewStack *stack, uint8_t identifier) | |
| virtual bool | hasChanged ()=0 |
| hasChanged indicates the current displayed view has changed. More... | |
| virtual void | shortButtonPress (xpcc::MenuButtons::Button button) |
| shortButtonPress handle the action for the pressed button | |
| bool | isAlive () const |
| isAlive tells the ViewStack if it should remove this screen. More... | |
| void | remove () |
| remove the view from the screen. More... | |
| uint8_t | getIdentifier () |
| getIdentifier of the view. | |
| xpcc::GraphicDisplay & | display () |
| virtual void | onRemove () |
| onRemove will be called right before the view gets deleted, can be reimplemented to reset external data. | |
| xpcc::ViewStack * | getViewStack () |
Protected Attributes | |
| xpcc::gui::GuiViewStack * | stack |
| Dimension | dimension |
| WidgetContainer | widgets |
| xpcc::gui::ColorPalette | colorpalette |
Additional Inherited Members | |
| Public Attributes inherited from xpcc::AbstractView | |
| const uint8_t | identifier |
| bool | alive |
| xpcc::gui::View::View | ( | xpcc::gui::GuiViewStack * | stack, |
| uint8_t | identifier, | ||
| xpcc::gui::Dimension | dimension | ||
| ) |
| stack | pointer to the stack, the screen should be displayed on. |
| identifier | can be used to determine which screen is the currently displayed on the graphicDisplay |
|
virtual |
Handles input events located in the parent GuiViewStack.
Reimplemented from xpcc::AbstractView.


Gateway
BOT
05-Jul-18 04:01 PM
[dscrdeql] <!!! Thorth !!!> and 10x
[dscrdeql] <AndronicusBass-Founder> Just thought a tip for helping explain tipsy begin
[dscrdeql] <!!! Thorth !!!> aaaa
[dscrdeql] <!!! Thorth !!!> 10x
(edited)
[dscrdeql] <!!! Thorth !!!> @webworker01 is neer me ... hmmm i must hurry up with my level
[dscrdeql] <AndronicusBass-Founder> where you at now?
(edited)
[dscrdeql] <SwimedDragon> I have a ps4 and I have a wii u, not going to buy the switch.
[dscrdeql] <SwimedDragon> At the time I was going to buy the wii u, there was news about the switch, and they said it was not going to replace the current gen (wii u), and that there would be games coming for both consoles.
[dscrdeql] <SwimedDragon> Offcourse, I bought the wii u, and not long after there was news that they we're stopping with the wii u. I bought several games for it that are now coming, or are on the switch.
[dscrdeql] <SwimedDragon> So I'm sticking with ps4 only this gen
[dscrdeql] <SwimedDragon> I wish I just did some research on it like 2 months later and I would have waited for the switch
[dscrdeql] <AndronicusBass-Founder> last year I bouth a xbox one s with a couple cames from microsoft store for 299, I had a ps4 already. I dont play games much these days except Ive spent hundreds if not thousands of hours playing tom clancy rainbox 6 vegas and vegas 2 on split screen co-op with my father the last 10 years
[dscrdeql] <SwimedDragon> Wow, I wish I had a father like that!
[dscrdeql] <SwimedDragon> Mines would never play games with me, maybe like once every 3 years.
[dscrdeql] <AndronicusBass-Founder> He likes war games
[dscrdeql] <SwimedDragon> But, I do have a wife that I play with on the ps4
[dscrdeql] <AndronicusBass-Founder> we have been known to play for many hours on end right through to the next day lol
[dscrdeql] <AndronicusBass-Founder> Nice
[dscrdeql] <AndronicusBass-Founder> Awesome wife
[dscrdeql] <!!! Thorth !!!> me where i am ?
[dscrdeql] <SwimedDragon> Did you also play Cod games with him?
[dscrdeql] <AndronicusBass-Founder> a couple of them. I miss split screen co-op not many of those these days. hopefully they will make a comeback
[dscrdeql] <sourge> 007 was a good split screen
[dscrdeql] <AndronicusBass-Founder> on n64. for sure
[dscrdeql] <AndronicusBass-Founder> I was in primary school when that was out
[dscrdeql] <SwimedDragon> Well, since online is available I don't like solo gaming anymore.
[dscrdeql] <SwimedDragon> We even bought a second ps4 (and also did with the ps3), so we could play lots of games together
[dscrdeql] <SwimedDragon> n64 and gamecube are probably the best I had. Lots of fun with the mario games with friends.
[dscrdeql] <donaldmathers> I own the original NES, the Super Nintendo and the Nintendo switch. Also got PS1-2-3 and ps4 lol
[dscrdeql] <donaldmathers> You could say I’m a gamer
[dscrdeql] <donaldmathers> Also got the ps vita
[dscrdeql] <AndronicusBass-Founder> I had a chipped ps1 but dont know what happened to it. Have 2 sega mega drives (genesis)
[dscrdeql] <SwimedDragon> I'm probably to young for the nes system.
[dscrdeql] <donaldmathers> I’m 33 so the nes and snes were my childhood loves
[dscrdeql] <AndronicusBass-Founder> I had a commodore 64 and the games were similar to nes
[dscrdeql] <AndronicusBass-Founder> took forever to load
[dscrdeql] <donaldmathers> Woww
[dscrdeql] <donaldmathers> That’s old school
[dscrdeql] <donaldmathers> Like atari
[dscrdeql] <AndronicusBass-Founder> was better than atari
[dscrdeql] <SwimedDragon> I think I did play some commodore 64 games, my father had it. But that's probably it
[dscrdeql] <donaldmathers> That Zelda game
[dscrdeql] <donaldmathers> That one
[dscrdeql] <donaldmathers> The absolute number one best
[dscrdeql] <donaldmathers> I adore that game so much
[dscrdeql] <donaldmathers> I bought the snes just for that game lol
[dscrdeql] <donaldmathers> Same with Nintendo switch
[dscrdeql] <donaldmathers> And original nes
[dscrdeql] <donaldmathers> All for Zelda lol
[dscrdeql] <SwimedDragon> I was more for the Mario games
[dscrdeql] <SwimedDragon> And for some mysterious reason, my son is playing those too
[dscrdeql] <Null> whats Zelda?
[dscrdeql] <skyfyre> Hahaha
[dscrdeql] <skyfyre> I was about to screenshot wikipedia for you
[dscrdeql] <AndronicusBass-Founder> whats that console?
The FC Twin (also known as the FC X2) is a Famiclone that can play Nintendo Entertainment System and Super NES games. Manufactured by Qishenglong (奇胜隆), the system has been well-received due to the increasing scarcity of the original hardware. It is distributed by Yobo ...
[dscrdeql] <Null>
[dscrdeql] <AndronicusBass-Founder> nice
[dscrdeql] <Null> i miss my sega saturn now
[dscrdeql] <Null>


the and The canal semicircular for lateral posterior cochlea anterior are that Figure than bony from canals labyrinth (Table ratio [], not with between arc line which was drawing ear common aspect cochlear vestibular endocast ancestral but both within length inner into digital []. vestibule labeled observed plane although other stereopair view; secondary ancestor radius total greater its body cochleae end largest among most over crus degree (Figure basal three volume fenestra calculated two Methods measured this Materials through Abbreviations high listed placental aqueduct channel deviation only section. angle species canaliculus slices all elliptical turn does dorsal equals more were reconstructed mammals position slender low However, diameter dimensions either used Table recess any similar labyrinthine planes region taxa canal, ampulla portion viewed extant mammal mass greatest has phylogenetic data larger (lc []–[]. here condition examined spiral Sus (Figures C). axis compared down limb ratios spherical well Chrysochloris contribution recent rotation vestibuli morphology when Rhinolophus less substantial Although Eumetopias Evol: Figures profile. 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Methods
(static) processLine(pose, line) → {Array}
Applies the specified input string to the pose and returns the
resulting pose and any output
Parameters:
| Name | Type | Description |
|---|---|---|
pose |
module:Pose.Pose | The initial pose |
line |
string | A line of input describing a command |
- Source:
Returns:
{[module:Pose.Pose, ?string}
The final pose, and the output line if any
- Type
- Array
(static) processLines(linesIn) → {Array}
Takes an array of strings, detailing the commands as the user has
specified them, processes them, and returns the final pose
and any output
Parameters:
| Name | Type | Description |
|---|---|---|
linesIn |
Array.<string> | The commands to be processed |
- Source:
Returns:
{[module:Pose.Pose, String[]]}
The final pose, and the output lines
- Type
- Array


{% extends "layout.html" %} {% block article_body %} {% markdown %} Innovative and fast-growing companies are the driving force of the European economy in the 21st century. They generate jobs and sustain Europe’s competitiveness. The [FT1000](https://www.ft.com/reports/europes-fastest-growing-companies) lists the 1,000 companies in Europe that have achieved the highest percentage growth in revenues between 2012 and 2015. Seventy-eight of the companies on the list are based in London, making the British capital [a hub for innovation and commerce](https://www.ft.com/content/a51ccce4-1a07-11e7-a266-12672483791a) in the EU even as the UK prepares to leave the bloc. Paris is second with 45 companies, followed by Milan with 34 and 32 in Berlin. See our [methodology note](#methodology) below or our [FAQ document](https://www.ft.com/content/a869a248-9a03-11e6-b8c6-568a43813464) for full details about how the list was compiled, or download a [CSV spreadsheet of the data](https://bertha.ig.ft.com/view/publish/dsv/1sLtLlvhaCDBgGSNskJPdKdkWJFwFNq0Er6GMGaEbt9c/data.csv). _Read the FT special report [FT1000: Europe's fastest growing companies](https://www.ft.com/reports/europes-fastest-growing-companies)._ {% endmarkdown %} {% include "ads/mid.html" %} {% include "table/index.html" %}


Introduction
Here's a quick overview of what JavaScript is, and what you'll need to start working with it.
If you're new to programming/scripting, JavaScript is a good place to start. Having said that, it's still quite different to HTML so I recommend you take your time and cover off a little bit each day. Don't worry if it takes you several days to complete - it's better to fully understand everything than to brush over it and not fully comprehend it.
I suggest you bookmark this tutorial now, then continue on.
What is JavaScript?
JavaScript is a scripting language that enables web developers/designers to build more functional and interactive websites.
Common uses of JavaScript include:
- Alert messages
- Popup windows
- Dynamic dropdown menus
- Form validation
- Displaying date/time
JavaScript usually runs on the client-side (the browser's side), as opposed to server-side (on the web server). One benefit of doing this is performance. On the client side, JavaScript is loaded into the browser and can run as soon as it is called. Without running on the client side, the page would need to refresh each time you needed a script to run.
What do I need to create JavaScript?
You can create JavaScript using the same equipment you use when creating HTML. That is:
- Computer
- Text editor. For example, Notepad (for Windows), Pico (for Linux), or Simpletext (Mac). You could use a HTML editor if you like but it's not needed.
- Web Browser. For example, Internet Explorer or Firefox. You will need to ensure JavaScript is enabled within your browser's settings (this is normally enabled by default).
The next lesson will show you how to enable/disable JavaScript in your browser.


automatically read vibrations in SMOL files
show vibration frequency list
Of course, all the other Jmol script commands work as well:


Choose the way to start teaming up!
“Talent wins games, but teamwork and intelligence win championships.”
Personal office
Get and change the info of your personal account.
Create a team
Create your own team and settle the tasks!
Join a team
Fing the team you'd like to contribute!


The National Rifle Association, one of Donald Trump’s largest allies, sent a 30-person delegation to Russia to meet with Putin’s Deputy Prime Minister, Dmitry Rogozin, who serves as head of Russia’s defense industry. The NRA claims that Rogozin, also chairman of the Russian Shooting Federation, provided a tour of the Federation's offices and they discussed a shooting tournament. This trip occurred the same week as Michael Flynn’s dinner with Putin.
Rogozin is well-known in Russia and the U.S. as the “radical” former leader of the ultra-right Rodina (“Motherland” in Russian) Party. In March 2014, the U.S. sanctioned him in retaliation for the invasion of Crimea and eastern Ukraine.
Source: The Daily Beast


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--- layout: default title: "Workshops" permalink: /workshops/2017/ year: 2017 location: "Paris" country: FR image_path: /assets/img/workshops/2017/ image: /assets/img/workshops/2017/Group_Picture_icon2017.jpeg ---
Date: May 16 to 19, 2017
Venue: Meudon Observatory, Paris
Hosts/organizers: Click here to download
Attendees: Click here to download
The attendees at the entrance of the 'Chateau' in the sunny Meudon observatory
The 2017 NYRIA workshop gathered 27 people from various locations. Informal presentation of the attendees on the first evening. The morning sessions were dedicated to 20min talks from each attendee to present their on-going research and extended coffee breaks and trigger disussions and collaborations between the attendees. The afternoon sessions were dedicated to invited talks and activites: Pierre Léna gave us a talk on "The revolutions of observations in Astrophysics".
Fascinating discussion with Pierre Léna about his career in instrumentation for astrophysics.
Arago refractive telescope.
The last morning was dedicated to a round table with Guy Perrin and Pierre Baudoz, after a presentation about the timeline of construction of the telescopes by Gabriele Rodighiero. The discussion was focused on "How to build an instrument today", with an emphasis on the big projects such as ELTs, gathering different countries and expertise.
Understanding the jargon behind the instrument design.
The slides of the Wrap-up session closing the meeting can be found here.
Final word for a successful workshop !
Written By: Faustine CantalloubeWe would like to warmly thanks our two funding parts, ASHRA and CIAS. In particular, David Mouillet for his trust in our project and Gérard Rousset and Yann Clénet for hosting us at LESIA. Thanks to Nicole Letourneur from CIAS, we benefited from amazing coffee breaks in the beautiful Urania room, which really helped creating nice interactions within the group. We also would like to thanks the Observatoire de Paris and Nicolas Lesté-Lasserre for providing us full access to the Observatoire de Paris for the historical visit of the site. We of course would like to thanks the speakers for sharing their time with us and for fruitful discussions: Pierre Léna, Guy Perrin, Pierre Baudoz and all the researchers from the LESIA who took their time to make us visit their optical labs.
ASHRA (Action Spécifique Haute Résolution Angulaire), website CIAS (Centre International d’Ateliers Scientifiques), website
Report for the CIAS (in French): click here Reports for the ASHRA: click here
Organization contact: F. Cantalloube<EMAIL_ADDRESS>On-site contact: E. Huby(elsa.huby@obspm.fr)


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My name is Micah Mutugi
Here is a list of projects that I have worked on
The project is a webpage of a travel agency called happy-trials.
It uses HTML and CSS languages
The project is about my-first-webpage.
It uses the HTML language
I am done with highschool and waiting to join university. I will be pursuing Bcom at Strathmore.
I also needed to challenge myself to do new things and what a better way than to learn code!
My hobbies currently are;


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For more information please contact us via email
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Introduction
Business Case
MyGroceries.com was a traditional grocery store that recently migrated to online sales, and due to COVID-19 is having an unexpected demand.
This company is hiring you as a Data Engineer to solve a data management problem due to high activity on purchase orders, and they would like to have a streamlined process that could communicate effectively and in real-time to the Purchase department, so they can anticipate to quick changes in their current stock.
You are proposing to use Kafka and a Confluent Stack of Technologies and this workshop will act as a showcase.
Full Credit to:
Confluent Inc. This workshop is based on Confluent Hybrid Cloud Workshop
Organizations are looking to become more event driven. The Confluent Platform is a streaming platform that can stream data, in real time, to the systems that need it, when they need it, across an entire organization. Processes that were once batch can now become real time, every event can be used to trigger other services and this can all be done using a common API with low latency and high throughput.
In this workshop we will explore how the Confluent Platform and Confluent Cloud can enable these architectures by building a real time supply and demand appplication using ksqlDB.
Lab 1: Connecting to your Workshop Environment
Your environment represents an on-premise data center and consists of a virtual machine hosted in the cloud running several docker containers. In a real world implementation, some of the components would be deployed differently but the logical data flow that we will be working on would remain the same.
Once your Docker stack is running, the following command confirms that you have several docker containers running
docker ps --format "table {{.ID}}\t{{.Names}}\t{{.RunningFor}}\t{{.Status}}"
You should see something similar to this.
CONTAINER ID NAMES CREATED STATUS
ef959fb9e566 dbtrans-simulator 3 minutes ago Up 3 minutes
979c20e26e60 ksqldb-cli 3 minutes ago Up 3 minutes
4eb1ba589357 control-center 3 minutes ago Up 3 minutes
c2362c0fc8e0 kafka-connect-onprem 3 minutes ago Up 3 minutes (healthy)
82086be4e239 schema-registry 3 minutes ago Up 3 minutes
62b037976674 broker 3 minutes ago Up 3 minutes
92c955b221f3 zookeeper 3 minutes ago Up 3 minutes
0794f9008bb6 mysql 3 minutes ago Up 3 minutes (healthy)
31c3c1372aa1 workshop-docs-webserver 3 minutes ago Up 3 minutes
|
Whenever you see this icon it means the step is mandatory. Missing one of these steps will result in the data pipeline not working as expected. |
|
Take advantage of the copy to clipboard button, it will save you some time! |
Lab 2: Getting Started
The primary data source for this workshop is a MySQL database running in your data center. Connected to this database is a orders application which we will discuss shortly.
Database Schema
The MySQL database contains a simple schema that includes Customer, Supplier, Product, Sales Order and Purchase Order information.
The idea behind this schema is simple, customers order products from a company and sales orders get created, the company then sends purchase orders to their suppliers so that product demand can be met by maintaining sensible stock levels.
We can inspect this schema further by logging into the MySQL CLI…
docker exec -it mysql bash -c 'mysql -u root -p$MYSQL_ROOT_PASSWORD --database orders'
…and viewing your tables
show tables;
There’s an extra table here called out_of_stock_events
that not in the schema diagram above, we’ll cover this table separately later on.
+--------------------------+
| Tables_in_orders |
+--------------------------+
| customers |
| out_of_stock_events |
| products |
| purchase_order_details |
| purchase_orders |
| sales_order_details |
| sales_orders |
| suppliers |
+--------------------------+
8 rows in set (0.00 sec)
Let’s view the row count for each table
SELECT * from (
SELECT 'customers' as table_name, COUNT(*) FROM customers
UNION
SELECT 'products' as table_name, COUNT(*) FROM products
UNION
SELECT 'suppliers' as table_name, COUNT(*) FROM suppliers
UNION
SELECT 'sales_orders' as table_name, COUNT(*) FROM sales_orders
UNION
SELECT 'sales_order_details' as table_name, COUNT(*) FROM sales_order_details
UNION
SELECT 'purchase_orders' as table_name, COUNT(*) FROM purchase_orders
UNION
SELECT 'purchase_order_details' as table_name, COUNT(*) FROM purchase_order_details
) row_counts;
As you can see, we have 30 customers, suppliers and products, 0 sales orders and 1 purchase order.
+------------------------+----------+
| table_name | COUNT(*) |
+------------------------+----------+
| customers | 30 |
| products | 30 |
| suppliers | 30 |
| sales_orders | 0 |
| sales_order_details | 0 |
| purchase_orders | 1 |
| purchase_order_details | 30 |
+------------------------+----------+
7 rows in set (0.00 sec)
The single purchase order was created so we have something in stock to sell, let’s have a look at what was ordered.
SELECT * FROM purchase_order_details;
+----+-------------------+------------+----------+------+
| id | purchase_order_id | product_id | quantity | cost |
+----+-------------------+------------+----------+------+
| 1 | 1 | 1 | 100 | 6.82 |
| 2 | 1 | 2 | 100 | 7.52 |
| 3 | 1 | 3 | 100 | 6.16 |
| 4 | 1 | 4 | 100 | 8.07 |
| 5 | 1 | 5 | 100 | 2.10 |
| 6 | 1 | 6 | 100 | 7.45 |
| 7 | 1 | 7 | 100 | 4.02 |
| 8 | 1 | 8 | 100 | 0.64 |
| 9 | 1 | 9 | 100 | 8.51 |
| 10 | 1 | 10 | 100 | 3.61 |
| 11 | 1 | 11 | 100 | 2.62 |
| 12 | 1 | 12 | 100 | 2.60 |
| 13 | 1 | 13 | 100 | 1.26 |
| 14 | 1 | 14 | 100 | 4.08 |
| 15 | 1 | 15 | 100 | 3.56 |
| 16 | 1 | 16 | 100 | 7.13 |
| 17 | 1 | 17 | 100 | 7.64 |
| 18 | 1 | 18 | 100 | 5.94 |
| 19 | 1 | 19 | 100 | 2.94 |
| 20 | 1 | 20 | 100 | 1.91 |
| 21 | 1 | 21 | 100 | 8.89 |
| 22 | 1 | 22 | 100 | 7.62 |
| 23 | 1 | 23 | 100 | 6.19 |
| 24 | 1 | 24 | 100 | 2.83 |
| 25 | 1 | 25 | 100 | 5.51 |
| 26 | 1 | 26 | 100 | 4.23 |
| 27 | 1 | 27 | 100 | 8.33 |
| 28 | 1 | 28 | 100 | 7.09 |
| 29 | 1 | 29 | 100 | 1.75 |
| 30 | 1 | 30 | 100 | 1.72 |
+----+-------------------+------------+----------+------+
30 rows in set (0.00 sec)
Here we have a single purchase order that is procuring 100 of each product, this reflects our initial and current stock levels.
Type exit
to leave the MySQL CLI
Starting the Orders Application
To start generating some sales orders we need to start the orders application. This application will continuously create new sales orders to simulate product demand. The application will also raise purchase orders when told to do so, we’ll cover this aspect later on in the workshop.
Start the orders application by running the following command.
|
Confirm that the simulator is working as expected
docker logs -f dbtrans-simulator
You should see an output like this:
Sales Order 1 Created
Sales Order 2 Created
Sales Order 3 Created
Sales Order 4 Created
Sales Order 5 Created
Sales Order 6 Created
...
Press ctrl-c
to quit
We now have sales_order
and sales_order_details
rows being created for us by the orders application.
Lab 3: Stream Events to Confluent Platform
Now that we have data being automatically created in our MySQL database it’s time to stream those changes into your on-premise Kafka cluster. We can do this using the Debezium MySQL Source connector
Create the MySQL source connector
We have a Kafka Connect worker already up and running in a docker container called kafka-connect-onprem
. This Kafka Connect worker is configured to connect to your on-premise Kafka cluster and has a internal REST server listening on port 18083
. We can create a connector from the command line using the cURL command. The cURL command allows us to send an HTTP POST request to the REST server, the '-H' option specifies the header of the request and includes the target host and port information, the -d
option specifies the data we will send, in this case its the configuration options for the connector. You can of course create and manage connectors using any tool or language capable of issuing HTTP requests.
In the launch_stack.sh
, you created the Debezium MySQL Source connector instance with the following command:-
|
We can confirm the connector is running by querying the REST interface
curl -s localhost:18083/connectors/mysql-source-connector/status | jq
You should see that the connector’s state is RUNNING
{
"name": "mysql-source-connector",
"connector": {
"state": "RUNNING",
"worker_id": "kafka-connect-onprem:18083"
},
"tasks": [
{
"id": 0,
"state": "RUNNING",
"worker_id": "kafka-connect-onprem:18083"
}
],
"type": "source"
}
View Messages in Confluent Control Center
Now that the MySQL source connector is up and running, we will be able to see messages appear in our local Kafka cluster.
We can use Confluent Control Center to confirm this.
Use the following and username and password to authenticate to Confluent Control Center
Username:
Password: your workshop password
On the landing page we can see that Confluent Control Center is monitoring two Kafka Clusters, our on-premise cluster and a Confluent Cloud Cluster
On the left hand navigation bar select "CO" (Controlcenter.cluster), this is your on-premise cluster.
Select the Topics Menu on the left
Select the workshop_sales_order_details
topic
Finally select the Messages tab and observe that messages are being streamed into Kafka from MySQL in real time.
Lab 4: Creating a ksqlDB Application
We now have all the data we need being streamed, in realtime, to Confluent Platform. You have a ksqlDB Server running inside a docker container that is configured to point to our Confluent Platform cluster.
Below is an illustration of the completed Supply & Demand ksqlDB Application, over the next few labs you will be building this step-by-step.
Start the ksqlDB CLI
To start the ksqlDB CLI run the following command:-
|
You should see something like this:-
===========================================
= _ _ ____ ____ =
= | | _____ __ _| | _ \| __ ) =
= | |/ / __|/ _` | | | | | _ \ =
= | <\__ \ (_| | | |_| | |_) | =
= |_|\_\___/\__, |_|____/|____/ =
= |_| =
= Event Streaming Database purpose-built =
= for stream processing apps =
===========================================
Copyright 2017-2020 Confluent Inc.
CLI v0.9.0, Server v0.9.0 located at http://ksqldb-server:8088
Having trouble? Type 'help' (case-insensitive) for a rundown of how things work!
ksql>
The ksqlDB CLI is pointing at a ksqlDB Server connected to your Confluent Platform instance.
To view a list of all topics in Confluent Platform run the following command:-
show topics;
You should see your own topics, _*
, along with topics from other workshop users.
ksql> show topics;
Kafka Topic | Partitions | Partition Replicas
---------------------------------------------------------------
workshop_customers | 1 | 3
workshop_products | 1 | 3
workshop_purchase_order_details | 1 | 3
workshop_purchase_orders | 1 | 3
workshop_sales_order_details | 1 | 3
workshop_sales_orders | 1 | 3
workshop_suppliers | 1 | 3
...
Inspect a topic's contents
To inspect the contents of a topic run the following:-
PRINT workshop_sales_orders;
You should see something similar:-
ksql> PRINT workshop_sales_orders;
Key format: AVRO
Value format: AVRO
rowtime: 2020/05/20 10:10:29.264 Z, key: {"id": 1}, value: {"id": 1, "order_date":<PHONE_NUMBER>000, "customer_id": 14, "sourcedc": ""}
rowtime: 2020/05/20 10:10:29.265 Z, key: {"id": 2}, value: {"id": 2, "order_date":<PHONE_NUMBER>000, "customer_id": 14, "sourcedc": ""}
rowtime: 2020/05/20 10:10:29.265 Z, key: {"id": 3}, value: {"id": 3, "order_date":<PHONE_NUMBER>000, "customer_id": 14, "sourcedc": ""}
rowtime: 2020/05/20 10:10:29.265 Z, key: {"id": 4}, value: {"id": 4, "order_date":<PHONE_NUMBER>000, "customer_id": 7, "sourcedc": ""}
...
Press ctrl-c
to stop
ksqlDB Streams
In order to work with a stream of data in ksqlDB we first need to register a stream over an existing topic.
We can do this using a CREATE STREAM
statement. Run the following command to create your first ksqlDB stream:-
|
You should see the following output
ksql> CREATE STREAM sales_orders WITH (KAFKA_TOPIC='workshop_sales_orders', VALUE_FORMAT='AVRO');
Message
----------------
Stream created
----------------
Create streams for each of your remaining topics
|
To view your current streams run the following command:-
SHOW STREAMS;
Notice that each stream is mapped to an underlying Kafka topic and that the format is AVRO.
Stream Name | Kafka Topic | Format
---------------------------------------------------------------
CUSTOMERS | workshop_customers | AVRO
PRODUCTS | workshop_products | AVRO
PURCHASE_ORDERS | workshop_purchase_orders | AVRO
PURCHASE_ORDER_DETAILS | workshop_purchase_order_details | AVRO
SALES_ORDERS | workshop_sales_orders | AVRO
SALES_ORDER_DETAILS | workshop_sales_order_details | AVRO
SUPPLIERS | workshop_suppliers | AVRO
---------------------------------------------------------------
To view the details of an individual topic you can you can use the describe
command:-
DESCRIBE sales_order_details;
Notice that all the columns have been created for us and we didn’t need to explicitly set their names and data types when we created the stream, this is one of the advantages of using AVRO and the Schema Registry.
Also notice that ksqlDB adds an implicit ROWKEY
column to every stream and table, which represents the corresponding Kafka message key.
Name : SALES_ORDER_DETAILS
Field | Type
-----------------------------------------
ROWKEY | VARCHAR(STRING) (key)
ID | INTEGER
SALES_ORDER_ID | INTEGER
PRODUCT_ID | INTEGER
QUANTITY | INTEGER
PRICE | DECIMAL
SOURCEDC | VARCHAR(STRING)
-----------------------------------------
For runtime statistics and query details run: DESCRIBE EXTENDED ;
|
Further Reading
|
Lab 5: Querying Streams with ksqlDB
There are two types of query in ksqlDB, Push queries and Pull queries.
-
Push Queries enable you to subscribe to a result as it changes in real-time. You can subscribe to the output of any query, including those that return a stream or a materialized aggregate table. The
EMIT CHANGES
clause is used to indicate a query is a push query. -
Pull Queries enable you to look up information at a point in time.
Another important point to understand is where within a stream a query starts to read from. You can control this behaviour using the ksql.streams.auto.offset.reset
property. This property can either be set to earliest
where data is consumed from the very beginning of the topic or latest
where only new data is consumed.
To see the current values for all properties run the following command
SHOW PROPERTIES;
Look out for a property called ksql.streams.auto.offset.reset
, it should currently be set to earliest
.
Property | Default override | Effective Value
--------------------------------------------------------------------------------------------
...
ksql.streams.auto.offset.reset | | earliest
...
--------------------------------------------------------------------------------------------
ksql>
You can override this setting to suit you needs:-
SET 'ksql.streams.auto.offset.reset'='earliest';
SET 'ksql.streams.auto.offset.reset'='latest';
Or preferably, using the abbreviated property names:-
SET 'auto.offset.reset' = 'latest';
SET 'auto.offset.reset' = 'earliest';
Let’s start by running a Push query and consume all messages from the beginning of a stream.
SET 'auto.offset.reset'='earliest';
SELECT id,
sales_order_id,
product_id,
quantity,
price
FROM sales_order_details
EMIT CHANGES;
You should see something similar to this:-
ksql> SELECT id, sales_order_id, product_id, quantity, price FROM sales_order_details EMIT CHANGES;
+-----------------+-----------------+-----------------+-----------------+-----------------+
|ID |SALES_ORDER_ID |PRODUCT_ID |QUANTITY |PRICE |
+-----------------+-----------------+-----------------+-----------------+-----------------+
|1 |1 |1 |10 |2.68 |
|2 |1 |23 |1 |9.01 |
|3 |1 |14 |6 |5.84 |
|4 |2 |12 |7 |4.00 |
|5 |2 |9 |4 |9.83 |
|6 |2 |5 |1 |8.81 |
|7 |2 |3 |8 |9.99 |
|8 |2 |1 |9 |2.68 |
|9 |3 |21 |5 |9.90 |
|10 |3 |2 |1 |8.23 |
|11 |3 |4 |2 |9.78 |
|12 |4 |15 |2 |6.16 |
...
...
|480 |157 |26 |5 |9.03 |
|481 |158 |2 |2 |8.23 |
|482 |159 |10 |4 |5.32 |
|483 |160 |25 |8 |9.00 |
Press ctrl-c
to stop
Notice that events continue to stream to the console until you explicitly cancel the query, this is because when we are working with streams in ksqlDB the data set is unbounded and could theoretically continue forever.
To inspect a bounded set of data, you can use the LIMIT
clause.
SELECT id,
sales_order_id,
product_id,
quantity,
price
FROM sales_order_details
EMIT CHANGES
LIMIT 10;
Here we are seeing the first 10 messages that were written to the topic. Notice that the query automatically terminates when the limit of 10 events is reached.
+-----------------+-----------------+-----------------+-----------------+-----------------+
|ID |SALES_ORDER_ID |PRODUCT_ID |QUANTITY |PRICE |
+-----------------+-----------------+-----------------+-----------------+-----------------+
|1 |1 |1 |10 |2.68 |
|2 |1 |23 |1 |9.01 |
|3 |1 |14 |6 |5.84 |
|4 |2 |12 |7 |4.00 |
|5 |2 |9 |4 |9.83 |
|6 |2 |5 |1 |8.81 |
|7 |2 |3 |8 |9.99 |
|8 |2 |1 |9 |2.68 |
|9 |3 |21 |5 |9.90 |
|10 |3 |2 |1 |8.23 |
Limit Reached
Query terminated
ksql>
Filtering Streams
Since ksqlDB is based on SQL, you can do many of the standard SQL things you’d expect to be able to do, including predicates and projections. The following query will return a stream of you the latest sales orders where the quantity
column is greater than 3.
SET 'auto.offset.reset'='latest';
SELECT id,
product_id,
quantity
FROM sales_order_details
WHERE quantity > 3
EMIT CHANGES;
You should only see events where the quantity
column value is greater than 3
.
+------------------------------+------------------------------+------------------------------+
|ID |PRODUCT_ID |QUANTITY |
+------------------------------+------------------------------+------------------------------+
|3153 |22 |8 |
|3154 |4 |6 |
|3155 |9 |4 |
|3156 |25 |10 |
|3158 |24 |8 |
|3159 |7 |4 |
|3161 |28 |8 |
|3162 |22 |7 |
|3163 |24 |6 |
|3165 |5 |8 |
|3167 |21 |9 |
Press ctrl-c
to stop
|
Further Reading
|
Lab 6: Creating ksqlDB tables
ksqlDB tables allow you to work the data in topics as key/value pairs, with a single value for each key. Tables can be created from an existing topic or from the query results from other tables or streams. You can read more about this here.
Creating Tables
We want to create tables over our customers
, suppliers
and products
streams so we can look up the current state for each customer, supplier and product. Later on we’ll be joining these tables to other streams. To successfully join to a table in ksqlDB you need to ensure that the table is keyed on the column you are going to use in the join. Our underlying topics already have the correct key set thanks to the Debezium MySQL connector configuration so we just need to use the PRIMARY KEY
clause when we create each table.
Create your 3 ksqlDB tables.
|
|
|
We can view our current tables using the following command:-
SHOW TABLES;
Table Name | Kafka Topic | Format | Windowed
----------------------------------------------------
CUSTOMERS_TBL | workshop_customers | AVRO | false
PRODUCTS_TBL | workshop_products | AVRO | false
SUPPLIERS_TBL | workshop_suppliers | AVRO | false
----------------------------------------------------
We’ll use these tables soon and join them to our streams.
|
Further Reading
|
Lab 7: Joining Steams & Tables with ksqlDB
We can join two streams together in ksqlDB using a windowed join. When using a windowed join, you must specify a windowing scheme by using the WITHIN
clause. A new input record on one side produces a join output for each matching record on the other side, and there can be multiple such matching records within a join window.
In the example below you can see that we are joining the sales_orders
stream to the sales_order_details
stream using a window of 1 seconds (INNER JOIN sales_order_details od WITHIN 1 SECONDS ON (o.id = od.sales_order_id)
). The orders application creates sales orders and their associated sales order detail rows at the same time, so 1 second will be plenty of time to ensure that a join takes place.
We are also joining to the customers_tbl
and products_tbl
tables
|
If we run a describe on this stream…
DESCRIBE sales_enriched;
…you’ll see that we have effectively denormalized the sales_orders
, sales_order_details
, customers
and products
streams/tables into a single event stream.
Name : SALES_ENRICHED
Field | Type
-------------------------------------------
ROWKEY | INTEGER (key)
ORDER_ID | INTEGER
ORDER_DETAILS_ID | INTEGER
ORDER_DATE | BIGINT
PRODUCT_ID | INTEGER
PRODUCT_NAME | VARCHAR(STRING)
PRODUCT_DESC | VARCHAR(STRING)
PRODUCT_PRICE | DECIMAL
PRODUCT_QTY | INTEGER
CUSTOMER_ID | INTEGER
CUSTOMER_FNAME | VARCHAR(STRING)
CUSTOMER_LNAME | VARCHAR(STRING)
CUSTOMER_EMAIL | VARCHAR(STRING)
CUSTOMER_CITY | VARCHAR(STRING)
CUSTOMER_COUNTRY | VARCHAR(STRING)
-------------------------------------------
We now need to create an equivalent purchases_enriched
stream that combines the purchase_orders
, purchase_order_details
, suppliers
and products
streams/tables. Since the purchases data model is very similar to that of the sales data model the query looks very similar.
|
If we run a describe on this stream…
DESCRIBE purchases_enriched;
Name : PURCHASES_ENRICHED
Field | Type
-------------------------------------------
ROWKEY | INTEGER (key)
ORDER_ID | INTEGER
ORDER_DETAILS_ID | INTEGER
ORDER_DATE | BIGINT
PRODUCT_ID | INTEGER
PRODUCT_NAME | VARCHAR(STRING)
PRODUCT_DESC | VARCHAR(STRING)
PRODUCT_COST | DECIMAL
PRODUCT_QTY | INTEGER
SUPPLIER_ID | INTEGER
SUPPLIER_NAME | VARCHAR(STRING)
SUPPLIER_EMAIL | VARCHAR(STRING)
SUPPLIER_CITY | VARCHAR(STRING)
SUPPLIER_COUNTRY | VARCHAR(STRING)
-------------------------------------------
…you’ll see that we have also denormalized the purchase_orders
, purchase_order_details
, suppliers
and products
streams/tables into a single event stream.
Let’s query the purchases_enriched
stream from the very beginning
SET 'auto.offset.reset'='earliest';
SELECT product_id,
product_name,
product_qty
FROM purchases_enriched
EMIT CHANGES;
Notice that the query returns the first 30 purchase order lines and then stops; this is because no purchase orders are being created by our orders application. The orders application will raise purchase orders for us when we send it some out of stock events.
+-------------------------+----------------------------------+------------------------+
|PRODUCT_ID |PRODUCT_NAME |PRODUCT_QTY |
+-------------------------+----------------------------------+------------------------+
|1 |Yogurt - Assorted Pack |100 |
|2 |Ostrich - Fan Fillet |100 |
|3 |Fish - Halibut, Cold Smoked |100 |
|4 |Tomatoes Tear Drop Yellow |100 |
|5 |Pasta - Fettuccine, Egg, Fresh |100 |
|6 |Plastic Wrap |100 |
|7 |Pineapple - Regular |100 |
|8 |Quail - Eggs, Fresh |100 |
|9 |Pork - Ground |100 |
|10 |Lamb Shoulder Boneless Nz |100 |
|11 |Sausage - Meat |100 |
|12 |Herb Du Provence - Primerba |100 |
|13 |Bread - Kimel Stick Poly |100 |
|14 |Food Colouring - Red |100 |
|15 |Cheese - Grie Des Champ |100 |
|16 |Longos - Lasagna Veg |100 |
|17 |Beets - Golden |100 |
|18 |Bread - Dark Rye |100 |
|19 |Pepperoni Slices |100 |
|20 |Glass - Wine, Plastic, Clear 5 Oz |100 |
|21 |Soup - Campbells, Beef Barley |100 |
|22 |Bread - Kimel Stick Poly |100 |
|23 |Plate - Foam, Bread And Butter |100 |
|24 |Parsley - Fresh |100 |
|25 |Cookie - Oreo 100x2 |100 |
|26 |Bread - Crusty Italian Poly |100 |
|27 |Wine - Chateauneuf Du Pape |100 |
|28 |Country Roll |100 |
|29 |Wine - Redchard Merritt |100 |
|30 |Doilies - 5, Paper |100 |
|
Further Reading
|
Lab 8: Streaming Current Stock Levels
Before we can create an out of stock event stream, we need to work out the current stock levels for each product. We can do this by combining the sales_enriched
stream with the purchases_enriched
stream and summing the sales_enriched.quantity
column (stock decrements) and the purchases_enriched.quantity
column (stock increments).
Let’s have a go at this now by creating a new stream called product_supply_and_demand
. This stream is consuming messages from the sales_enriched
stream and included the product_id
and quantity
column converted to a negative value, we do this because sales events are our demand and hence decrement stock.
|
Let’s have a quick look at the first few rows of this stream
SET 'auto.offset.reset'='earliest';
SELECT product_id,
quantity
FROM product_supply_and_demand
EMIT CHANGES
LIMIT 10;
This query shows a history of all sales and their affect on stock levels.
+------------------------------------------+------------------------------------------+
|PRODUCT_ID |QUANTITY |
+------------------------------------------+------------------------------------------+
|1 |-6 |
|15 |-3 |
|14 |-7 |
|23 |-3 |
|13 |-10 |
|4 |-9 |
|10 |-9 |
|15 |-8 |
|10 |-2 |
|27 |-7 |
Limit Reached
Query terminated
What we need to do now is also include all product purchases in the same stream. We can do this using an INSERT INTO
statement. The INSERT INTO
statement streams the result of a SELECT query into an existing stream and its underlying topic.
|
Our product_supply_and_demand
now includes all product sales as stock decrements and all product purchases as stock increments.
We can see the demand for a single product by filtering on the product_id
and including only events where the quantity
is less than zero, i.e. stock decrements.
SET 'auto.offset.reset'='earliest';
SELECT product_id,
quantity
FROM product_supply_and_demand
WHERE product_id = 15
AND quantity < 0
EMIT CHANGES;
+------------------------------------------+------------------------------------------+
|PRODUCT_ID |QUANTITY |
+------------------------------------------+------------------------------------------+
|1 |-6 |
|1 |-9 |
|1 |-7 |
|1 |-5 |
|1 |-1 |
|1 |-7 |
|1 |-7 |
|1 |-10 |
|1 |-8 |
|1 |-4 |
|1 |-2 |
...
...
...
We can also see the supply for a single product by filtering on the product_id
and including only events where the quantity
is greater than zero, i.e. stock increments.
SET 'auto.offset.reset'='earliest';
SELECT product_id,
quantity
FROM product_supply_and_demand
WHERE product_id = 15
AND quantity > 0
EMIT CHANGES;
This query will only return a single event and reflects the initial purchase order line that was raised for this product.
+------------------------------------------+------------------------------------------+
|PRODUCT_ID |QUANTITY |
+------------------------------------------+------------------------------------------+
|1 |100 |
We’re now is a position where we can calculate the current stock level for each product. We can do this by creating a table that groups by the product_id
and sums up the quantity
column which contains both stock decrements and stock increments.
|
When we query this table with a Push query…
SET 'auto.offset.reset'='latest';
SELECT product_id,
stock_level
FROM current_stock
EMIT CHANGES;
…each new event that is displayed on the console reflects the current stock level for the associated product, a new event will be emitted each time a product’s stock level changes. Depending on how long it took you to get to this point in the workshop, you may see that all your stock levels are negative. This is because, apart from the initial purchase order for 100 of each product, we have not created any more purchase orders and our customers will have their orders on hold until we acquire more stock, not good, but we’ll fix that soon.
Lab 9: Pull Queries
We can now run our first Pull query. Pull queries are used against tables with aggregates and can only query a single key.
To run a Pull query we just query the table as normal but drop the EMIT CHANGES
clause. In this query we are asking "what is the current stock level for product id 1?"
select product_id, stock_level from current_stock where rowkey=15;
The query will return the current stock level and immediatly terminate.
+----------------------------------------------------+----------------------------------------------------+
|PRODUCT_ID |STOCK_LEVEL |
+----------------------------------------------------+----------------------------------------------------+
|1 |-67 |
Query terminated
We can also use the ksqlDB Server’s REST endpoint to make Pull queries.
Exit from the ksqlDB CLI and run the following from the command line.
curl -s -X "POST" "http://localhost:8088/query" -H "Content-Type: application/vnd.ksql.v1+json; charset=utf-8" -d $'{ "ksql": "select product_id, stock_level from current_stock where rowkey=15;" }'| jq .
As you can see, the ksqlDB Server’s REST endpoint will return a JSON message with the product_id
and its current stock_level
. This is useful for applications that want access to the current state of the world using a request/response type pattern.
[
{
"header": {
"queryId": "query_1582892390468",
"schema": "`PRODUCT_ID` INTEGER, STOCK_LEVEL
INTEGER"
}
},
{
"row": {
"columns": [
1,
-76
]
}
}
]
|
Further Reading
|
Lab 10: Streaming Recent Product Demand
Now that we know the current stock level is for each product, we can use this information to send an event to the orders application and ask it to create purchase orders to replenish the stock, but how much should we stock should we order? we could just order enough to satisfy the current backlog but we’d quickly run out of stock again.
What we really want to do is order enough to satisfy the backlog and enough to meet future demand, we can make an attempt at predicting what the future demand will be by looking at the past.
In the following query we are creating a table that will calculate the demand for each product over the last 3 minutes using a WINDOW HOPPING
clause.
Hopping windows are based on time intervals. They model fixed-sized, possibly overlapping windows. A hopping window is defined by two properties: the window’s duration and its advance, or “hop”, interval. The advance interval specifies how far a window moves forward in time relative to the previous window. In our query we we have a window with a duration of three minutes and an advance interval of one minute. Because hopping windows can overlap, a record can belong to more than one such window.
Start the ksqlDB CLI if you haven’t already
docker exec -it ksqldb-cli ksql http://ksqldb-server:8088
Create the windowed query
|
If we query this table for a single product…
SET 'auto.offset.reset'='latest';
SELECT window_start_time,
window_end_time,
product_id,
demand_last_3mins
FROM product_demand_last_3mins_tbl
WHERE product_id = 15
EMIT CHANGES;
…you’ll see the start and end times for each three minute window, along with the product demand for those 3 minutes. Notice how the window start times are staggered by one minute, this is the advance interval in action. As new sales events occur a new message will be displayed with an update to the window(s) total.
+-----------------+-----------------+-----------------+-----------------+
|WINDOW_START_TIME|WINDOW_END_TIME |PRODUCT_ID |DEMAND_LAST_3MINS|
+-----------------+-----------------+-----------------+-----------------+
|13:33:00 |13:36:00 |1 |10 |
|13:34:00 |13:37:00 |1 |10 |
|13:35:00 |13:38:00 |1 |1 |
|13:33:00 |13:36:00 |1 |11 |
|13:34:00 |13:37:00 |1 |11 |
|13:35:00 |13:38:00 |1 |2 |
|13:34:00 |13:37:00 |1 |21 |
|13:35:00 |13:38:00 |1 |12 |
|13:36:00 |13:39:00 |1 |10 |
|13:34:00 |13:37:00 |1 |26 |
|13:35:00 |13:38:00 |1 |17 |
|13:36:00 |13:39:00 |1 |15 |
|13:35:00 |13:38:00 |1 |22 |
|13:36:00 |13:39:00 |1 |20 |
|13:37:00 |13:40:00 |1 |5 |
|13:36:00 |13:39:00 |1 |28 |
|13:37:00 |13:40:00 |1 |13 |
|13:38:00 |13:41:00 |1 |8 |
We will now create a stream from this table and then join it to the current_stock
table
Create a stream from the table’s underlying topic…
|
|
Further Reading
|
Lab 11: Streaming "Out of Stock" Events
Now that we have the current_stock
table and product_demand_last_3mins
stream, we can create a out_of_stock_events
stream by joining the two together and calculating the required purchase order quantity. We calculate the purchase_qty
from adding the inverse of the current stock level to the last 3 minutes of demand. The stream is filtered to only include products that have negative stock and therefore need purchase orders raising for them.
But before we do that, let’s open a couple more terminal sessions and start the ksqlDB CLI in each.
ssh @
docker exec -it ksqldb-cli ksql http://ksqldb-server:8088
Execute the following query in the 2nd session…
SET 'auto.offset.reset'='latest';
SELECT product_id,
stock_level
FROM current_stock
EMIT CHANGES;
…and this query in the 3rd session
SET 'auto.offset.reset'='latest';
SELECT product_id,
product_qty
FROM purchases_enriched
EMIT CHANGES;
Now create the stream...
|
When we query the out_of_stock_events
stream…
SET 'auto.offset.reset' = 'latest';
SELECT product_id,
window_start_time,
window_end_time,
stock_level,
demand_last_3mins,
quantity_to_purchase
FROM out_of_stock_events
EMIT CHANGES;
…you’ll see a constant stream of out of stock products and the predicted purchase quantity that should be ordered to satisfy any current backlog and also meet the next 3 minutes demand.
+----------------+------------------+----------------+-------------+-----------------+-------------------+
|PRODUCT_ID |WINDOW_START_TIME |WINDOW_END_TIME |STOCK_LEVEL |DEMAND_LAST_3MINS|QUANTITY_TO_PURCASE|
+----------------+------------------+----------------+-------------+-----------------+-------------------+
|28 |13:53:00 |13:56:00 |-85 |12 |97 |
|28 |13:54:00 |13:57:00 |-85 |1 |86 |
|28 |13:55:00 |13:58:00 |-85 |1 |86 |
|4 |13:53:00 |13:56:00 |-128 |26 |154 |
|4 |13:54:00 |13:57:00 |-128 |11 |139 |
|4 |13:55:00 |13:58:00 |-128 |11 |139 |
|5 |13:53:00 |13:56:00 |-73 |15 |88 |
|5 |13:54:00 |13:57:00 |-73 |15 |88 |
|5 |13:55:00 |13:58:00 |-73 |15 |88 |
|28 |13:53:00 |13:56:00 |-85 |18 |103 |
|28 |13:54:00 |13:57:00 |-91 |7 |98 |
|28 |13:55:00 |13:58:00 |-91 |7 |98 |
|14 |13:53:00 |13:56:00 |-156 |31 |187 |
|14 |13:54:00 |13:57:00 |-156 |15 |171 |
|14 |13:55:00 |13:58:00 |-156 |6 |162 |
|5 |13:53:00 |13:56:00 |-73 |25 |98 |
|5 |13:54:00 |13:57:00 |-83 |25 |108 |
|5 |13:55:00 |13:58:00 |-83 |25 |108 |
|12 |13:53:00 |13:56:00 |-197 |25 |222 |
|12 |13:54:00 |13:57:00 |-197 |21 |218 |
|12 |13:55:00 |13:58:00 |-200 |3 |203 |
...
...
Since we have a sink connector in place, the out_of_stock_events
topic goes directly into a MySQL database table, the on-premise application will then process these events and create purchase order for us.
You now have a real time view of the current product stock levels in the second ksqlDB session and the purchases being made to replenish the stock in third session.
Observe the current stock query in the second ksqlDB session, when a product has zero or less stock you should see a purchase event appear in the third ksqlDB session and then the new stock level reflected in the second session. In theory, given a constant demand, each product should run out of stock and get replenished roughly every 3 minutes.
|
Further Reading
|
EXTENSIONS_PLACEHOLDER
Wrapping up
During this workshop we have seen how the Confluent Platform and Confluent Cloud can be used to build event driven, real time applications that span the data center and public cloud.